{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "085feda9",
   "metadata": {},
   "source": [
    "## Resnet on CIFAR100\n",
    "\n",
    "在[resnet.ipynb](./resnet.ipynb)中我们测试了Resnet在Imagenette上的分类任务, Imagenette是只包含10类的ImageNet的子集,这里我们在CIFAR100上测试对比不同的Resnet网络效果.\n",
    "\n",
    "CIFAR-100（加拿大高级研究所）数据集是 CIFAR-10 数据集的重要扩展，由100个不同类别的 60,000 张 32x32 彩色图像组成。它由 CIFAR 研究所的研究人员开发，为更复杂的机器学习和计算机视觉任务提供了更具挑战性的数据集。\n",
    "\n",
    "随即挑选的10个类别:\n",
    "\n",
    "![alt text](resources/cifar100.png \"Title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e15b3c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c08db432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Basic Info of train dataaset: \n",
      " Dataset CIFAR100\n",
      "    Number of datapoints: 50000\n",
      "    Root location: /home/tf/workspace/hands-dirty-on-dl/dataset\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               RandomResize(size=[34, 36, 38, 40, 42, 44], interpolation=bilinear, max_size=None, antialias=True)\n",
      "               RandomRotation(degrees=[-3.0, 3.0], interpolation=nearest, expand=False, fill=0)\n",
      "               RandomCrop(size=(32, 32), padding=None)\n",
      "               RandomHorizontalFlip(p=0.5)\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.50707516, 0.48654887, 0.44091784], std=[0.26733429, 0.25643846, 0.27615047])\n",
      "           )\n",
      "Basic Info of test dataset: \n",
      " Dataset CIFAR100\n",
      "    Number of datapoints: 10000\n",
      "    Root location: /home/tf/workspace/hands-dirty-on-dl/dataset\n",
      "    Split: Test\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.50707516, 0.48654887, 0.44091784], std=[0.26733429, 0.25643846, 0.27615047])\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "from hdd.data_util.transforms import RandomResize\n",
    "\n",
    "# 我们提前计算好了训练数据集上的均值和方差\n",
    "TRAIN_MEAN = [0.50707516, 0.48654887, 0.44091784]\n",
    "TRAIN_STD = [0.26733429, 0.25643846, 0.27615047]\n",
    "\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([34, 36, 38, 40, 42, 44]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomRotation(3),\n",
    "        transforms.RandomCrop(32),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "# 加载数据集\n",
    "train_dataset = datasets.CIFAR100(\n",
    "    root=DATA_ROOT,\n",
    "    train=True,\n",
    "    transform=train_dataset_transforms,\n",
    "    download=True,\n",
    ")\n",
    "val_dataset = datasets.CIFAR100(\n",
    "    root=DATA_ROOT,\n",
    "    train=False,\n",
    "    transform=transforms.Compose(\n",
    "        [transforms.ToTensor(), transforms.Normalize(TRAIN_MEAN, TRAIN_STD)]\n",
    "    ),\n",
    "    download=True,\n",
    ")\n",
    "print(\"Basic Info of train dataaset: \\n\", train_dataset)\n",
    "print(\"Basic Info of test dataset: \\n\", val_dataset)\n",
    "BATCH_SIZE = 64\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    ")\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9ad906",
   "metadata": {},
   "source": [
    "## 测试比较不同的Resnet架构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ccc11ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/280 Train Loss: 4.0435 Accuracy: 0.0754 Time: 13.68249  | Val Loss: 3.6238 Accuracy: 0.1338\n",
      "Epoch: 2/280 Train Loss: 3.5900 Accuracy: 0.1449 Time: 12.49647  | Val Loss: 3.2665 Accuracy: 0.2010\n",
      "Epoch: 3/280 Train Loss: 3.3088 Accuracy: 0.1981 Time: 12.53381  | Val Loss: 2.9849 Accuracy: 0.2619\n",
      "Epoch: 4/280 Train Loss: 3.0510 Accuracy: 0.2419 Time: 12.97166  | Val Loss: 2.7808 Accuracy: 0.2937\n",
      "Epoch: 5/280 Train Loss: 2.8054 Accuracy: 0.2900 Time: 12.42250  | Val Loss: 2.5803 Accuracy: 0.3359\n",
      "Epoch: 6/280 Train Loss: 2.5945 Accuracy: 0.3317 Time: 12.42958  | Val Loss: 2.3953 Accuracy: 0.3798\n",
      "Epoch: 7/280 Train Loss: 2.4273 Accuracy: 0.3682 Time: 12.38133  | Val Loss: 2.3451 Accuracy: 0.3924\n",
      "Epoch: 8/280 Train Loss: 2.2999 Accuracy: 0.3952 Time: 12.40817  | Val Loss: 2.1389 Accuracy: 0.4342\n",
      "Epoch: 9/280 Train Loss: 2.1937 Accuracy: 0.4180 Time: 12.98203  | Val Loss: 2.0193 Accuracy: 0.4604\n",
      "Epoch: 10/280 Train Loss: 2.0942 Accuracy: 0.4433 Time: 13.12972  | Val Loss: 2.0900 Accuracy: 0.4547\n",
      "Epoch: 11/280 Train Loss: 2.0262 Accuracy: 0.4595 Time: 12.65093  | Val Loss: 1.9077 Accuracy: 0.4904\n",
      "Epoch: 12/280 Train Loss: 1.9533 Accuracy: 0.4751 Time: 12.84280  | Val Loss: 1.9230 Accuracy: 0.4889\n",
      "Epoch: 13/280 Train Loss: 1.8977 Accuracy: 0.4898 Time: 12.62308  | Val Loss: 1.9528 Accuracy: 0.4845\n",
      "Epoch: 14/280 Train Loss: 1.8365 Accuracy: 0.5040 Time: 12.89471  | Val Loss: 1.8716 Accuracy: 0.5093\n",
      "Epoch: 15/280 Train Loss: 1.7818 Accuracy: 0.5159 Time: 12.51257  | Val Loss: 1.9354 Accuracy: 0.4957\n",
      "Epoch: 16/280 Train Loss: 1.7484 Accuracy: 0.5271 Time: 12.67483  | Val Loss: 1.9052 Accuracy: 0.4963\n",
      "Epoch: 17/280 Train Loss: 1.7191 Accuracy: 0.5327 Time: 12.86078  | Val Loss: 1.7993 Accuracy: 0.5223\n",
      "Epoch: 18/280 Train Loss: 1.6831 Accuracy: 0.5456 Time: 12.66281  | Val Loss: 1.8465 Accuracy: 0.5163\n",
      "Epoch: 19/280 Train Loss: 1.6396 Accuracy: 0.5543 Time: 12.93032  | Val Loss: 1.8433 Accuracy: 0.5209\n",
      "Epoch: 20/280 Train Loss: 1.6234 Accuracy: 0.5567 Time: 12.69349  | Val Loss: 1.8604 Accuracy: 0.5124\n",
      "Epoch: 21/280 Train Loss: 1.5994 Accuracy: 0.5646 Time: 12.78300  | Val Loss: 1.7958 Accuracy: 0.5264\n",
      "Epoch: 22/280 Train Loss: 1.5676 Accuracy: 0.5699 Time: 12.56473  | Val Loss: 1.7868 Accuracy: 0.5302\n",
      "Epoch: 23/280 Train Loss: 1.5560 Accuracy: 0.5741 Time: 13.00756  | Val Loss: 1.7003 Accuracy: 0.5485\n",
      "Epoch: 24/280 Train Loss: 1.5280 Accuracy: 0.5811 Time: 12.69731  | Val Loss: 1.7349 Accuracy: 0.5499\n",
      "Epoch: 25/280 Train Loss: 1.5165 Accuracy: 0.5865 Time: 12.45797  | Val Loss: 1.8815 Accuracy: 0.5266\n",
      "Epoch: 26/280 Train Loss: 1.4928 Accuracy: 0.5924 Time: 12.48166  | Val Loss: 1.8186 Accuracy: 0.5380\n",
      "Epoch: 27/280 Train Loss: 1.4846 Accuracy: 0.5910 Time: 13.07326  | Val Loss: 1.7959 Accuracy: 0.5370\n",
      "Epoch: 28/280 Train Loss: 1.4719 Accuracy: 0.5975 Time: 12.91746  | Val Loss: 1.6872 Accuracy: 0.5496\n",
      "Epoch: 29/280 Train Loss: 1.4564 Accuracy: 0.6007 Time: 12.81850  | Val Loss: 1.7357 Accuracy: 0.5493\n",
      "Epoch: 30/280 Train Loss: 1.4324 Accuracy: 0.6069 Time: 12.80994  | Val Loss: 1.8774 Accuracy: 0.5283\n",
      "Epoch: 31/280 Train Loss: 1.4239 Accuracy: 0.6080 Time: 12.82816  | Val Loss: 1.7739 Accuracy: 0.5486\n",
      "Epoch: 32/280 Train Loss: 1.4069 Accuracy: 0.6125 Time: 12.85250  | Val Loss: 1.6904 Accuracy: 0.5630\n",
      "Epoch: 33/280 Train Loss: 1.3999 Accuracy: 0.6139 Time: 12.78816  | Val Loss: 1.7741 Accuracy: 0.5415\n",
      "Epoch: 34/280 Train Loss: 1.3959 Accuracy: 0.6159 Time: 12.63222  | Val Loss: 1.7430 Accuracy: 0.5542\n",
      "Epoch: 35/280 Train Loss: 1.3719 Accuracy: 0.6212 Time: 12.60686  | Val Loss: 1.6400 Accuracy: 0.5740\n",
      "Epoch: 36/280 Train Loss: 1.3714 Accuracy: 0.6223 Time: 12.74412  | Val Loss: 1.6172 Accuracy: 0.5790\n",
      "Epoch: 37/280 Train Loss: 1.3626 Accuracy: 0.6223 Time: 12.57121  | Val Loss: 1.6704 Accuracy: 0.5629\n",
      "Epoch: 38/280 Train Loss: 1.3588 Accuracy: 0.6268 Time: 12.22263  | Val Loss: 1.6042 Accuracy: 0.5776\n",
      "Epoch: 39/280 Train Loss: 1.3513 Accuracy: 0.6277 Time: 12.28199  | Val Loss: 1.6161 Accuracy: 0.5775\n",
      "Epoch: 40/280 Train Loss: 1.3424 Accuracy: 0.6302 Time: 12.18537  | Val Loss: 1.7595 Accuracy: 0.5577\n",
      "Epoch: 41/280 Train Loss: 1.3244 Accuracy: 0.6349 Time: 12.21784  | Val Loss: 1.5579 Accuracy: 0.5856\n",
      "Epoch: 42/280 Train Loss: 1.3210 Accuracy: 0.6358 Time: 12.17812  | Val Loss: 1.8478 Accuracy: 0.5503\n",
      "Epoch: 43/280 Train Loss: 1.3177 Accuracy: 0.6369 Time: 12.28418  | Val Loss: 2.0776 Accuracy: 0.5066\n",
      "Epoch: 44/280 Train Loss: 1.3180 Accuracy: 0.6385 Time: 12.30748  | Val Loss: 1.8923 Accuracy: 0.5299\n",
      "Epoch: 45/280 Train Loss: 1.2997 Accuracy: 0.6417 Time: 12.28368  | Val Loss: 1.8782 Accuracy: 0.5362\n",
      "Epoch: 46/280 Train Loss: 1.3018 Accuracy: 0.6421 Time: 12.18695  | Val Loss: 1.9034 Accuracy: 0.5338\n",
      "Epoch: 47/280 Train Loss: 1.2981 Accuracy: 0.6415 Time: 12.18470  | Val Loss: 1.7433 Accuracy: 0.5611\n",
      "Epoch: 48/280 Train Loss: 1.2891 Accuracy: 0.6421 Time: 12.18553  | Val Loss: 1.8536 Accuracy: 0.5397\n",
      "Epoch: 49/280 Train Loss: 1.2774 Accuracy: 0.6481 Time: 12.18989  | Val Loss: 1.8125 Accuracy: 0.5563\n",
      "Epoch: 50/280 Train Loss: 1.2834 Accuracy: 0.6467 Time: 12.34229  | Val Loss: 1.7171 Accuracy: 0.5676\n",
      "Epoch: 51/280 Train Loss: 1.2714 Accuracy: 0.6480 Time: 12.17763  | Val Loss: 1.8609 Accuracy: 0.5455\n",
      "Epoch: 52/280 Train Loss: 1.2665 Accuracy: 0.6501 Time: 12.22528  | Val Loss: 1.5411 Accuracy: 0.5887\n",
      "Epoch: 53/280 Train Loss: 1.2675 Accuracy: 0.6502 Time: 12.29676  | Val Loss: 1.6548 Accuracy: 0.5742\n",
      "Epoch: 54/280 Train Loss: 1.2541 Accuracy: 0.6537 Time: 12.20251  | Val Loss: 1.5504 Accuracy: 0.6017\n",
      "Epoch: 55/280 Train Loss: 1.2578 Accuracy: 0.6540 Time: 12.23037  | Val Loss: 1.7257 Accuracy: 0.5698\n",
      "Epoch: 56/280 Train Loss: 1.2540 Accuracy: 0.6539 Time: 12.34391  | Val Loss: 2.3819 Accuracy: 0.4967\n",
      "Epoch: 57/280 Train Loss: 1.2505 Accuracy: 0.6535 Time: 12.23608  | Val Loss: 1.7916 Accuracy: 0.5662\n",
      "Epoch: 58/280 Train Loss: 1.2410 Accuracy: 0.6570 Time: 12.26325  | Val Loss: 1.6429 Accuracy: 0.5839\n",
      "Epoch: 59/280 Train Loss: 1.2351 Accuracy: 0.6587 Time: 12.20584  | Val Loss: 1.7991 Accuracy: 0.5486\n",
      "Epoch: 60/280 Train Loss: 1.2342 Accuracy: 0.6604 Time: 12.20184  | Val Loss: 1.7133 Accuracy: 0.5666\n",
      "Epoch: 61/280 Train Loss: 1.2354 Accuracy: 0.6602 Time: 12.22934  | Val Loss: 1.5406 Accuracy: 0.6026\n",
      "Epoch: 62/280 Train Loss: 1.2317 Accuracy: 0.6606 Time: 12.26314  | Val Loss: 1.7736 Accuracy: 0.5538\n",
      "Epoch: 63/280 Train Loss: 1.2261 Accuracy: 0.6604 Time: 12.33418  | Val Loss: 1.7954 Accuracy: 0.5503\n",
      "Epoch: 64/280 Train Loss: 1.2269 Accuracy: 0.6597 Time: 12.20843  | Val Loss: 1.8384 Accuracy: 0.5577\n",
      "Epoch: 65/280 Train Loss: 1.2161 Accuracy: 0.6651 Time: 12.18059  | Val Loss: 1.8225 Accuracy: 0.5442\n",
      "Epoch: 66/280 Train Loss: 1.2267 Accuracy: 0.6619 Time: 12.18906  | Val Loss: 1.6483 Accuracy: 0.5772\n",
      "Epoch: 67/280 Train Loss: 1.2159 Accuracy: 0.6642 Time: 12.19745  | Val Loss: 1.6967 Accuracy: 0.5643\n",
      "Epoch: 68/280 Train Loss: 1.2137 Accuracy: 0.6651 Time: 12.22245  | Val Loss: 1.6867 Accuracy: 0.5744\n",
      "Epoch: 69/280 Train Loss: 1.2146 Accuracy: 0.6642 Time: 12.23195  | Val Loss: 1.8098 Accuracy: 0.5612\n",
      "Epoch: 70/280 Train Loss: 1.2067 Accuracy: 0.6682 Time: 12.31759  | Val Loss: 1.7439 Accuracy: 0.5674\n",
      "Epoch: 71/280 Train Loss: 0.8412 Accuracy: 0.7587 Time: 12.24264  | Val Loss: 1.4492 Accuracy: 0.6403\n",
      "Epoch: 72/280 Train Loss: 0.7107 Accuracy: 0.7921 Time: 12.23612  | Val Loss: 1.4241 Accuracy: 0.6499\n",
      "Epoch: 73/280 Train Loss: 0.6474 Accuracy: 0.8095 Time: 12.22754  | Val Loss: 1.3832 Accuracy: 0.6543\n",
      "Epoch: 74/280 Train Loss: 0.6194 Accuracy: 0.8183 Time: 12.20100  | Val Loss: 1.4170 Accuracy: 0.6569\n",
      "Epoch: 75/280 Train Loss: 0.5886 Accuracy: 0.8242 Time: 12.17422  | Val Loss: 1.4202 Accuracy: 0.6621\n",
      "Epoch: 76/280 Train Loss: 0.5529 Accuracy: 0.8358 Time: 12.17231  | Val Loss: 1.4610 Accuracy: 0.6582\n",
      "Epoch: 77/280 Train Loss: 0.5302 Accuracy: 0.8415 Time: 12.19387  | Val Loss: 1.4395 Accuracy: 0.6623\n",
      "Epoch: 78/280 Train Loss: 0.5128 Accuracy: 0.8463 Time: 12.18702  | Val Loss: 1.4176 Accuracy: 0.6742\n",
      "Epoch: 79/280 Train Loss: 0.4901 Accuracy: 0.8531 Time: 12.17729  | Val Loss: 1.4461 Accuracy: 0.6684\n",
      "Epoch: 80/280 Train Loss: 0.4735 Accuracy: 0.8576 Time: 12.18909  | Val Loss: 1.4315 Accuracy: 0.6706\n",
      "Epoch: 81/280 Train Loss: 0.4545 Accuracy: 0.8614 Time: 12.18149  | Val Loss: 1.4944 Accuracy: 0.6637\n",
      "Epoch: 82/280 Train Loss: 0.4425 Accuracy: 0.8654 Time: 12.25143  | Val Loss: 1.4517 Accuracy: 0.6731\n",
      "Epoch: 83/280 Train Loss: 0.4248 Accuracy: 0.8698 Time: 12.26687  | Val Loss: 1.4941 Accuracy: 0.6678\n",
      "Epoch: 84/280 Train Loss: 0.4194 Accuracy: 0.8733 Time: 12.28185  | Val Loss: 1.5209 Accuracy: 0.6651\n",
      "Epoch: 85/280 Train Loss: 0.4055 Accuracy: 0.8780 Time: 12.18885  | Val Loss: 1.4901 Accuracy: 0.6658\n",
      "Epoch: 86/280 Train Loss: 0.3951 Accuracy: 0.8790 Time: 12.20132  | Val Loss: 1.4692 Accuracy: 0.6724\n",
      "Epoch: 87/280 Train Loss: 0.3841 Accuracy: 0.8845 Time: 12.18334  | Val Loss: 1.5539 Accuracy: 0.6658\n",
      "Epoch: 88/280 Train Loss: 0.3662 Accuracy: 0.8878 Time: 12.17619  | Val Loss: 1.5211 Accuracy: 0.6749\n",
      "Epoch: 89/280 Train Loss: 0.3580 Accuracy: 0.8903 Time: 12.17908  | Val Loss: 1.5768 Accuracy: 0.6729\n",
      "Epoch: 90/280 Train Loss: 0.3508 Accuracy: 0.8911 Time: 12.17750  | Val Loss: 1.5675 Accuracy: 0.6736\n",
      "Epoch: 91/280 Train Loss: 0.3357 Accuracy: 0.8954 Time: 12.18731  | Val Loss: 1.5340 Accuracy: 0.6716\n",
      "Epoch: 92/280 Train Loss: 0.3398 Accuracy: 0.8968 Time: 12.19459  | Val Loss: 1.6226 Accuracy: 0.6657\n",
      "Epoch: 93/280 Train Loss: 0.3259 Accuracy: 0.9007 Time: 12.17826  | Val Loss: 1.6482 Accuracy: 0.6627\n",
      "Epoch: 94/280 Train Loss: 0.3247 Accuracy: 0.9015 Time: 12.18003  | Val Loss: 1.6277 Accuracy: 0.6663\n",
      "Epoch: 95/280 Train Loss: 0.3083 Accuracy: 0.9055 Time: 12.19830  | Val Loss: 1.6087 Accuracy: 0.6691\n",
      "Epoch: 96/280 Train Loss: 0.3064 Accuracy: 0.9058 Time: 12.18671  | Val Loss: 1.5893 Accuracy: 0.6717\n",
      "Epoch: 97/280 Train Loss: 0.2997 Accuracy: 0.9098 Time: 12.25054  | Val Loss: 1.5798 Accuracy: 0.6742\n",
      "Epoch: 98/280 Train Loss: 0.2901 Accuracy: 0.9099 Time: 12.28880  | Val Loss: 1.6894 Accuracy: 0.6587\n",
      "Epoch: 99/280 Train Loss: 0.2867 Accuracy: 0.9116 Time: 12.17832  | Val Loss: 1.6344 Accuracy: 0.6639\n",
      "Epoch: 100/280 Train Loss: 0.2826 Accuracy: 0.9143 Time: 12.18880  | Val Loss: 1.6775 Accuracy: 0.6655\n",
      "Epoch: 101/280 Train Loss: 0.2718 Accuracy: 0.9162 Time: 12.19249  | Val Loss: 1.6645 Accuracy: 0.6680\n",
      "Epoch: 102/280 Train Loss: 0.2734 Accuracy: 0.9152 Time: 12.20020  | Val Loss: 1.7164 Accuracy: 0.6648\n",
      "Epoch: 103/280 Train Loss: 0.2673 Accuracy: 0.9174 Time: 12.17912  | Val Loss: 1.7313 Accuracy: 0.6620\n",
      "Epoch: 104/280 Train Loss: 0.2571 Accuracy: 0.9204 Time: 12.18519  | Val Loss: 1.7431 Accuracy: 0.6617\n",
      "Epoch: 105/280 Train Loss: 0.2592 Accuracy: 0.9201 Time: 12.18391  | Val Loss: 1.7884 Accuracy: 0.6557\n",
      "Epoch: 106/280 Train Loss: 0.2508 Accuracy: 0.9230 Time: 12.17049  | Val Loss: 1.7116 Accuracy: 0.6659\n",
      "Epoch: 107/280 Train Loss: 0.2490 Accuracy: 0.9233 Time: 12.17753  | Val Loss: 1.7621 Accuracy: 0.6664\n",
      "Epoch: 108/280 Train Loss: 0.2416 Accuracy: 0.9264 Time: 12.17854  | Val Loss: 1.6785 Accuracy: 0.6671\n",
      "Epoch: 109/280 Train Loss: 0.2449 Accuracy: 0.9258 Time: 12.18810  | Val Loss: 1.7329 Accuracy: 0.6645\n",
      "Epoch: 110/280 Train Loss: 0.2367 Accuracy: 0.9261 Time: 12.17122  | Val Loss: 1.7668 Accuracy: 0.6637\n",
      "Epoch: 111/280 Train Loss: 0.2300 Accuracy: 0.9294 Time: 12.17910  | Val Loss: 1.7723 Accuracy: 0.6594\n",
      "Epoch: 112/280 Train Loss: 0.2282 Accuracy: 0.9305 Time: 12.22291  | Val Loss: 1.8375 Accuracy: 0.6580\n",
      "Epoch: 113/280 Train Loss: 0.2199 Accuracy: 0.9333 Time: 12.16867  | Val Loss: 1.8127 Accuracy: 0.6587\n",
      "Epoch: 114/280 Train Loss: 0.2197 Accuracy: 0.9335 Time: 12.17677  | Val Loss: 1.9130 Accuracy: 0.6577\n",
      "Epoch: 115/280 Train Loss: 0.2185 Accuracy: 0.9342 Time: 12.20335  | Val Loss: 1.8138 Accuracy: 0.6614\n",
      "Epoch: 116/280 Train Loss: 0.2132 Accuracy: 0.9347 Time: 12.26234  | Val Loss: 1.9007 Accuracy: 0.6535\n",
      "Epoch: 117/280 Train Loss: 0.2146 Accuracy: 0.9348 Time: 12.17443  | Val Loss: 1.8509 Accuracy: 0.6558\n",
      "Epoch: 118/280 Train Loss: 0.2033 Accuracy: 0.9375 Time: 12.17801  | Val Loss: 1.8670 Accuracy: 0.6511\n",
      "Epoch: 119/280 Train Loss: 0.2020 Accuracy: 0.9374 Time: 12.19084  | Val Loss: 1.8811 Accuracy: 0.6535\n",
      "Epoch: 120/280 Train Loss: 0.2051 Accuracy: 0.9381 Time: 12.16309  | Val Loss: 1.8542 Accuracy: 0.6567\n",
      "Epoch: 121/280 Train Loss: 0.2016 Accuracy: 0.9382 Time: 12.19158  | Val Loss: 1.8800 Accuracy: 0.6528\n",
      "Epoch: 122/280 Train Loss: 0.2048 Accuracy: 0.9371 Time: 12.21996  | Val Loss: 1.9119 Accuracy: 0.6483\n",
      "Epoch: 123/280 Train Loss: 0.1954 Accuracy: 0.9387 Time: 12.29843  | Val Loss: 1.8147 Accuracy: 0.6640\n",
      "Epoch: 124/280 Train Loss: 0.1959 Accuracy: 0.9405 Time: 12.17971  | Val Loss: 1.8499 Accuracy: 0.6598\n",
      "Epoch: 125/280 Train Loss: 0.1976 Accuracy: 0.9399 Time: 12.18975  | Val Loss: 1.9861 Accuracy: 0.6471\n",
      "Epoch: 126/280 Train Loss: 0.1958 Accuracy: 0.9405 Time: 12.18068  | Val Loss: 1.8658 Accuracy: 0.6535\n",
      "Epoch: 127/280 Train Loss: 0.1915 Accuracy: 0.9426 Time: 12.19316  | Val Loss: 1.9020 Accuracy: 0.6515\n",
      "Epoch: 128/280 Train Loss: 0.1882 Accuracy: 0.9430 Time: 12.17523  | Val Loss: 1.9377 Accuracy: 0.6581\n",
      "Epoch: 129/280 Train Loss: 0.1872 Accuracy: 0.9427 Time: 12.18646  | Val Loss: 1.9431 Accuracy: 0.6545\n",
      "Epoch: 130/280 Train Loss: 0.1783 Accuracy: 0.9443 Time: 12.19189  | Val Loss: 1.8787 Accuracy: 0.6581\n",
      "Epoch: 131/280 Train Loss: 0.1851 Accuracy: 0.9433 Time: 12.19351  | Val Loss: 1.9306 Accuracy: 0.6512\n",
      "Epoch: 132/280 Train Loss: 0.1774 Accuracy: 0.9459 Time: 12.19704  | Val Loss: 1.8982 Accuracy: 0.6576\n",
      "Epoch: 133/280 Train Loss: 0.1802 Accuracy: 0.9444 Time: 12.18808  | Val Loss: 1.9054 Accuracy: 0.6558\n",
      "Epoch: 134/280 Train Loss: 0.1788 Accuracy: 0.9455 Time: 12.21997  | Val Loss: 1.7937 Accuracy: 0.6659\n",
      "Epoch: 135/280 Train Loss: 0.1788 Accuracy: 0.9467 Time: 12.18441  | Val Loss: 1.9505 Accuracy: 0.6475\n",
      "Epoch: 136/280 Train Loss: 0.1760 Accuracy: 0.9467 Time: 12.18813  | Val Loss: 1.9250 Accuracy: 0.6592\n",
      "Epoch: 137/280 Train Loss: 0.1742 Accuracy: 0.9462 Time: 12.20805  | Val Loss: 2.0106 Accuracy: 0.6518\n",
      "Epoch: 138/280 Train Loss: 0.1800 Accuracy: 0.9458 Time: 12.18469  | Val Loss: 1.9943 Accuracy: 0.6548\n",
      "Epoch: 139/280 Train Loss: 0.1763 Accuracy: 0.9471 Time: 12.17787  | Val Loss: 2.0021 Accuracy: 0.6530\n",
      "Epoch: 140/280 Train Loss: 0.1702 Accuracy: 0.9483 Time: 12.17181  | Val Loss: 1.9714 Accuracy: 0.6565\n",
      "Epoch: 141/280 Train Loss: 0.1429 Accuracy: 0.9574 Time: 12.24635  | Val Loss: 1.8402 Accuracy: 0.6697\n",
      "Epoch: 142/280 Train Loss: 0.1254 Accuracy: 0.9629 Time: 12.19394  | Val Loss: 1.8746 Accuracy: 0.6690\n",
      "Epoch: 143/280 Train Loss: 0.1152 Accuracy: 0.9655 Time: 12.24380  | Val Loss: 1.8624 Accuracy: 0.6685\n",
      "Epoch: 144/280 Train Loss: 0.1120 Accuracy: 0.9660 Time: 12.29031  | Val Loss: 1.8396 Accuracy: 0.6702\n",
      "Epoch: 145/280 Train Loss: 0.1048 Accuracy: 0.9680 Time: 12.17055  | Val Loss: 1.7928 Accuracy: 0.6799\n",
      "Epoch: 146/280 Train Loss: 0.1038 Accuracy: 0.9687 Time: 12.25834  | Val Loss: 1.8609 Accuracy: 0.6736\n",
      "Epoch: 147/280 Train Loss: 0.1005 Accuracy: 0.9698 Time: 12.21160  | Val Loss: 1.8949 Accuracy: 0.6720\n",
      "Epoch: 148/280 Train Loss: 0.1012 Accuracy: 0.9700 Time: 12.18870  | Val Loss: 1.8609 Accuracy: 0.6730\n",
      "Epoch: 149/280 Train Loss: 0.1005 Accuracy: 0.9701 Time: 12.22261  | Val Loss: 1.8839 Accuracy: 0.6730\n",
      "Epoch: 150/280 Train Loss: 0.0937 Accuracy: 0.9727 Time: 12.20899  | Val Loss: 1.8929 Accuracy: 0.6697\n",
      "Epoch: 151/280 Train Loss: 0.0938 Accuracy: 0.9722 Time: 12.21950  | Val Loss: 1.9036 Accuracy: 0.6694\n",
      "Epoch: 152/280 Train Loss: 0.0932 Accuracy: 0.9718 Time: 12.18053  | Val Loss: 1.8938 Accuracy: 0.6712\n",
      "Epoch: 153/280 Train Loss: 0.0924 Accuracy: 0.9712 Time: 12.18145  | Val Loss: 1.8896 Accuracy: 0.6744\n",
      "Early stop at epoch 153!\n",
      "#Parameter: 11220132 Accuracy: 0.6544\n"
     ]
    }
   ],
   "source": [
    "from spacy import training\n",
    "from hdd.models.cnn.resnet import ResnetSmall, resnet18_config\n",
    "from hdd.train.early_stopping import EarlyStoppingInMem\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    resnet_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout,\n",
    "    lr,\n",
    "    weight_decay,\n",
    "    step_size=70,\n",
    "    gamma=0.1,\n",
    "    patience=80,\n",
    "    max_epochs=280,\n",
    ") -> tuple[ResnetSmall, dict[str, list[float]]]:\n",
    "    net = ResnetSmall(resnet_config, num_classes=100, dropout=dropout).to(DEVICE)\n",
    "    criteria = nn.CrossEntropyLoss()\n",
    "    # SGD的收敛速度远不如Adam好\n",
    "    # optimizer = torch.optim.SGD(\n",
    "    #     net.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay\n",
    "    # )\n",
    "    optimizer = optim.AdamW(\n",
    "        net.parameters(), lr=lr, eps=1e-6, weight_decay=weight_decay\n",
    "    )\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "        optimizer, step_size=step_size, gamma=gamma, last_epoch=-1\n",
    "    )\n",
    "    early_stopper = EarlyStoppingInMem(patience=patience, verbose=False)\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        early_stopper,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "net, resnet18_stats = train_net(\n",
    "    resnet18_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-2,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59f07e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/280 Train Loss: 4.0897 Accuracy: 0.0696 Time: 18.51071  | Val Loss: 3.6693 Accuracy: 0.1304\n",
      "Epoch: 2/280 Train Loss: 3.6279 Accuracy: 0.1400 Time: 18.50532  | Val Loss: 3.2372 Accuracy: 0.2079\n",
      "Epoch: 3/280 Train Loss: 3.3119 Accuracy: 0.1935 Time: 18.66785  | Val Loss: 2.9802 Accuracy: 0.2569\n",
      "Epoch: 4/280 Train Loss: 3.0319 Accuracy: 0.2424 Time: 18.54803  | Val Loss: 2.9218 Accuracy: 0.2700\n",
      "Epoch: 5/280 Train Loss: 2.8046 Accuracy: 0.2878 Time: 18.51891  | Val Loss: 2.4757 Accuracy: 0.3475\n",
      "Epoch: 6/280 Train Loss: 2.6241 Accuracy: 0.3244 Time: 18.59367  | Val Loss: 2.4744 Accuracy: 0.3574\n",
      "Epoch: 7/280 Train Loss: 2.4662 Accuracy: 0.3567 Time: 18.56074  | Val Loss: 2.2597 Accuracy: 0.4060\n",
      "Epoch: 8/280 Train Loss: 2.3434 Accuracy: 0.3858 Time: 18.52589  | Val Loss: 2.3390 Accuracy: 0.3949\n",
      "Epoch: 9/280 Train Loss: 2.2194 Accuracy: 0.4128 Time: 18.54563  | Val Loss: 2.1835 Accuracy: 0.4265\n",
      "Epoch: 10/280 Train Loss: 2.1288 Accuracy: 0.4334 Time: 18.51725  | Val Loss: 2.0608 Accuracy: 0.4541\n",
      "Epoch: 11/280 Train Loss: 2.0520 Accuracy: 0.4522 Time: 18.54115  | Val Loss: 1.8651 Accuracy: 0.4956\n",
      "Epoch: 12/280 Train Loss: 1.9721 Accuracy: 0.4681 Time: 18.51047  | Val Loss: 1.9457 Accuracy: 0.4897\n",
      "Epoch: 13/280 Train Loss: 1.9135 Accuracy: 0.4878 Time: 18.66874  | Val Loss: 2.0836 Accuracy: 0.4670\n",
      "Epoch: 14/280 Train Loss: 1.8711 Accuracy: 0.4947 Time: 18.53353  | Val Loss: 1.7964 Accuracy: 0.5123\n",
      "Epoch: 15/280 Train Loss: 1.8095 Accuracy: 0.5096 Time: 18.59476  | Val Loss: 1.8479 Accuracy: 0.5105\n",
      "Epoch: 16/280 Train Loss: 1.7645 Accuracy: 0.5213 Time: 18.58957  | Val Loss: 1.7646 Accuracy: 0.5252\n",
      "Epoch: 17/280 Train Loss: 1.7264 Accuracy: 0.5292 Time: 18.55842  | Val Loss: 1.6943 Accuracy: 0.5351\n",
      "Epoch: 18/280 Train Loss: 1.6869 Accuracy: 0.5417 Time: 18.57775  | Val Loss: 1.8012 Accuracy: 0.5282\n",
      "Epoch: 19/280 Train Loss: 1.6665 Accuracy: 0.5478 Time: 18.58486  | Val Loss: 1.7743 Accuracy: 0.5332\n",
      "Epoch: 20/280 Train Loss: 1.6344 Accuracy: 0.5550 Time: 18.57857  | Val Loss: 1.7685 Accuracy: 0.5352\n",
      "Epoch: 21/280 Train Loss: 1.6004 Accuracy: 0.5631 Time: 18.58572  | Val Loss: 1.6554 Accuracy: 0.5549\n",
      "Epoch: 22/280 Train Loss: 1.5765 Accuracy: 0.5691 Time: 18.56629  | Val Loss: 1.7921 Accuracy: 0.5313\n",
      "Epoch: 23/280 Train Loss: 1.5624 Accuracy: 0.5744 Time: 18.55882  | Val Loss: 1.6985 Accuracy: 0.5546\n",
      "Epoch: 24/280 Train Loss: 1.5374 Accuracy: 0.5803 Time: 18.54113  | Val Loss: 1.7882 Accuracy: 0.5454\n",
      "Epoch: 25/280 Train Loss: 1.5174 Accuracy: 0.5866 Time: 18.53595  | Val Loss: 1.7775 Accuracy: 0.5377\n",
      "Epoch: 26/280 Train Loss: 1.5030 Accuracy: 0.5900 Time: 18.52932  | Val Loss: 1.6919 Accuracy: 0.5560\n",
      "Epoch: 27/280 Train Loss: 1.4770 Accuracy: 0.5960 Time: 18.51326  | Val Loss: 1.7536 Accuracy: 0.5396\n",
      "Epoch: 28/280 Train Loss: 1.4652 Accuracy: 0.6006 Time: 18.51897  | Val Loss: 1.7437 Accuracy: 0.5506\n",
      "Epoch: 29/280 Train Loss: 1.4458 Accuracy: 0.6028 Time: 18.52804  | Val Loss: 1.9864 Accuracy: 0.5087\n",
      "Epoch: 30/280 Train Loss: 1.4203 Accuracy: 0.6114 Time: 18.52054  | Val Loss: 1.8047 Accuracy: 0.5402\n",
      "Epoch: 31/280 Train Loss: 1.4225 Accuracy: 0.6132 Time: 18.51887  | Val Loss: 1.7427 Accuracy: 0.5590\n",
      "Epoch: 32/280 Train Loss: 1.4109 Accuracy: 0.6132 Time: 18.51761  | Val Loss: 1.7722 Accuracy: 0.5569\n",
      "Epoch: 33/280 Train Loss: 1.3865 Accuracy: 0.6205 Time: 18.51414  | Val Loss: 1.7692 Accuracy: 0.5563\n",
      "Epoch: 34/280 Train Loss: 1.3812 Accuracy: 0.6234 Time: 18.54889  | Val Loss: 1.8056 Accuracy: 0.5532\n",
      "Epoch: 35/280 Train Loss: 1.3813 Accuracy: 0.6226 Time: 18.52271  | Val Loss: 1.7306 Accuracy: 0.5567\n",
      "Epoch: 36/280 Train Loss: 1.3558 Accuracy: 0.6280 Time: 18.52919  | Val Loss: 1.8096 Accuracy: 0.5386\n",
      "Epoch: 37/280 Train Loss: 1.3540 Accuracy: 0.6293 Time: 18.51639  | Val Loss: 1.8078 Accuracy: 0.5503\n",
      "Epoch: 38/280 Train Loss: 1.3400 Accuracy: 0.6318 Time: 18.52498  | Val Loss: 1.7825 Accuracy: 0.5490\n",
      "Epoch: 39/280 Train Loss: 1.3334 Accuracy: 0.6357 Time: 18.54022  | Val Loss: 1.7203 Accuracy: 0.5564\n",
      "Epoch: 40/280 Train Loss: 1.3314 Accuracy: 0.6338 Time: 18.53046  | Val Loss: 1.6157 Accuracy: 0.5854\n",
      "Epoch: 41/280 Train Loss: 1.3121 Accuracy: 0.6407 Time: 18.53462  | Val Loss: 1.7601 Accuracy: 0.5482\n",
      "Epoch: 42/280 Train Loss: 1.3092 Accuracy: 0.6409 Time: 18.51003  | Val Loss: 1.7069 Accuracy: 0.5693\n",
      "Epoch: 43/280 Train Loss: 1.2926 Accuracy: 0.6418 Time: 18.57934  | Val Loss: 1.5636 Accuracy: 0.5963\n",
      "Epoch: 44/280 Train Loss: 1.2944 Accuracy: 0.6456 Time: 18.54805  | Val Loss: 1.7921 Accuracy: 0.5527\n",
      "Epoch: 45/280 Train Loss: 1.2931 Accuracy: 0.6463 Time: 18.62266  | Val Loss: 1.6888 Accuracy: 0.5652\n",
      "Epoch: 46/280 Train Loss: 1.2840 Accuracy: 0.6461 Time: 18.54106  | Val Loss: 1.5856 Accuracy: 0.5840\n",
      "Epoch: 47/280 Train Loss: 1.2735 Accuracy: 0.6525 Time: 18.54316  | Val Loss: 2.0000 Accuracy: 0.5316\n",
      "Epoch: 48/280 Train Loss: 1.2789 Accuracy: 0.6513 Time: 18.52686  | Val Loss: 1.6114 Accuracy: 0.5823\n",
      "Epoch: 49/280 Train Loss: 1.2663 Accuracy: 0.6524 Time: 18.53255  | Val Loss: 1.7836 Accuracy: 0.5587\n",
      "Epoch: 50/280 Train Loss: 1.2544 Accuracy: 0.6557 Time: 18.57489  | Val Loss: 1.6435 Accuracy: 0.5877\n",
      "Epoch: 51/280 Train Loss: 1.2539 Accuracy: 0.6568 Time: 18.53234  | Val Loss: 1.6835 Accuracy: 0.5727\n",
      "Epoch: 52/280 Train Loss: 1.2495 Accuracy: 0.6561 Time: 18.52268  | Val Loss: 1.5461 Accuracy: 0.5937\n",
      "Epoch: 53/280 Train Loss: 1.2546 Accuracy: 0.6556 Time: 18.53221  | Val Loss: 1.7062 Accuracy: 0.5743\n",
      "Epoch: 54/280 Train Loss: 1.2395 Accuracy: 0.6564 Time: 18.51920  | Val Loss: 1.6164 Accuracy: 0.5866\n",
      "Epoch: 55/280 Train Loss: 1.2361 Accuracy: 0.6628 Time: 18.51206  | Val Loss: 1.5738 Accuracy: 0.5958\n",
      "Epoch: 56/280 Train Loss: 1.2247 Accuracy: 0.6611 Time: 18.56892  | Val Loss: 1.6305 Accuracy: 0.5858\n",
      "Epoch: 57/280 Train Loss: 1.2294 Accuracy: 0.6609 Time: 18.60230  | Val Loss: 1.6268 Accuracy: 0.5825\n",
      "Epoch: 58/280 Train Loss: 1.2181 Accuracy: 0.6659 Time: 18.57675  | Val Loss: 1.6226 Accuracy: 0.5801\n",
      "Epoch: 59/280 Train Loss: 1.2126 Accuracy: 0.6664 Time: 18.57458  | Val Loss: 1.6565 Accuracy: 0.5747\n",
      "Epoch: 60/280 Train Loss: 1.2152 Accuracy: 0.6682 Time: 18.56640  | Val Loss: 1.7830 Accuracy: 0.5537\n",
      "Epoch: 61/280 Train Loss: 1.2095 Accuracy: 0.6679 Time: 18.55180  | Val Loss: 1.6368 Accuracy: 0.5938\n",
      "Epoch: 62/280 Train Loss: 1.2040 Accuracy: 0.6693 Time: 18.54513  | Val Loss: 1.6361 Accuracy: 0.5850\n",
      "Epoch: 63/280 Train Loss: 1.2085 Accuracy: 0.6687 Time: 18.56349  | Val Loss: 1.5424 Accuracy: 0.5968\n",
      "Epoch: 64/280 Train Loss: 1.1919 Accuracy: 0.6709 Time: 18.53227  | Val Loss: 1.7656 Accuracy: 0.5708\n",
      "Epoch: 65/280 Train Loss: 1.1940 Accuracy: 0.6702 Time: 18.53187  | Val Loss: 1.7514 Accuracy: 0.5602\n",
      "Epoch: 66/280 Train Loss: 1.1989 Accuracy: 0.6706 Time: 18.52808  | Val Loss: 1.5703 Accuracy: 0.6034\n",
      "Epoch: 67/280 Train Loss: 1.1910 Accuracy: 0.6718 Time: 18.59318  | Val Loss: 1.5396 Accuracy: 0.6083\n",
      "Epoch: 68/280 Train Loss: 1.1997 Accuracy: 0.6706 Time: 18.56196  | Val Loss: 1.4925 Accuracy: 0.6103\n",
      "Epoch: 69/280 Train Loss: 1.1785 Accuracy: 0.6753 Time: 18.54824  | Val Loss: 1.6600 Accuracy: 0.5893\n",
      "Epoch: 70/280 Train Loss: 1.1805 Accuracy: 0.6762 Time: 18.51714  | Val Loss: 1.6056 Accuracy: 0.5919\n",
      "Epoch: 71/280 Train Loss: 0.8049 Accuracy: 0.7715 Time: 18.57336  | Val Loss: 1.2820 Accuracy: 0.6742\n",
      "Epoch: 72/280 Train Loss: 0.6640 Accuracy: 0.8067 Time: 18.52155  | Val Loss: 1.2760 Accuracy: 0.6794\n",
      "Epoch: 73/280 Train Loss: 0.5984 Accuracy: 0.8256 Time: 18.54204  | Val Loss: 1.2781 Accuracy: 0.6815\n",
      "Epoch: 74/280 Train Loss: 0.5648 Accuracy: 0.8353 Time: 18.56919  | Val Loss: 1.2671 Accuracy: 0.6886\n",
      "Epoch: 75/280 Train Loss: 0.5311 Accuracy: 0.8417 Time: 18.59182  | Val Loss: 1.3415 Accuracy: 0.6781\n",
      "Epoch: 76/280 Train Loss: 0.4984 Accuracy: 0.8517 Time: 18.58958  | Val Loss: 1.2649 Accuracy: 0.6983\n",
      "Epoch: 77/280 Train Loss: 0.4793 Accuracy: 0.8562 Time: 18.53599  | Val Loss: 1.3151 Accuracy: 0.6876\n",
      "Epoch: 78/280 Train Loss: 0.4564 Accuracy: 0.8660 Time: 18.57882  | Val Loss: 1.2984 Accuracy: 0.6962\n",
      "Epoch: 79/280 Train Loss: 0.4380 Accuracy: 0.8684 Time: 18.54557  | Val Loss: 1.3529 Accuracy: 0.6869\n",
      "Epoch: 80/280 Train Loss: 0.4106 Accuracy: 0.8759 Time: 18.53418  | Val Loss: 1.3165 Accuracy: 0.6949\n",
      "Epoch: 81/280 Train Loss: 0.3967 Accuracy: 0.8796 Time: 18.51994  | Val Loss: 1.4373 Accuracy: 0.6873\n",
      "Epoch: 82/280 Train Loss: 0.3831 Accuracy: 0.8841 Time: 18.52175  | Val Loss: 1.3613 Accuracy: 0.6958\n",
      "Epoch: 83/280 Train Loss: 0.3721 Accuracy: 0.8883 Time: 18.51608  | Val Loss: 1.3567 Accuracy: 0.6961\n",
      "Epoch: 84/280 Train Loss: 0.3615 Accuracy: 0.8906 Time: 18.55030  | Val Loss: 1.4034 Accuracy: 0.6943\n",
      "Epoch: 85/280 Train Loss: 0.3396 Accuracy: 0.8966 Time: 18.53524  | Val Loss: 1.4156 Accuracy: 0.6961\n",
      "Epoch: 86/280 Train Loss: 0.3278 Accuracy: 0.9001 Time: 18.51557  | Val Loss: 1.4543 Accuracy: 0.6949\n",
      "Epoch: 87/280 Train Loss: 0.3194 Accuracy: 0.9040 Time: 18.52729  | Val Loss: 1.4304 Accuracy: 0.7010\n",
      "Epoch: 88/280 Train Loss: 0.3191 Accuracy: 0.9041 Time: 18.54045  | Val Loss: 1.4878 Accuracy: 0.6928\n",
      "Epoch: 89/280 Train Loss: 0.3031 Accuracy: 0.9084 Time: 18.52162  | Val Loss: 1.3957 Accuracy: 0.7009\n",
      "Epoch: 90/280 Train Loss: 0.2927 Accuracy: 0.9097 Time: 18.52779  | Val Loss: 1.5432 Accuracy: 0.6862\n",
      "Epoch: 91/280 Train Loss: 0.2803 Accuracy: 0.9139 Time: 18.54365  | Val Loss: 1.5155 Accuracy: 0.6962\n",
      "Epoch: 92/280 Train Loss: 0.2774 Accuracy: 0.9165 Time: 18.53279  | Val Loss: 1.5449 Accuracy: 0.6885\n",
      "Epoch: 93/280 Train Loss: 0.2767 Accuracy: 0.9158 Time: 18.53629  | Val Loss: 1.5023 Accuracy: 0.6920\n",
      "Epoch: 94/280 Train Loss: 0.2590 Accuracy: 0.9212 Time: 18.51604  | Val Loss: 1.5118 Accuracy: 0.6979\n",
      "Epoch: 95/280 Train Loss: 0.2554 Accuracy: 0.9220 Time: 18.61625  | Val Loss: 1.5757 Accuracy: 0.6936\n",
      "Epoch: 96/280 Train Loss: 0.2463 Accuracy: 0.9259 Time: 18.57181  | Val Loss: 1.5532 Accuracy: 0.6957\n",
      "Epoch: 97/280 Train Loss: 0.2406 Accuracy: 0.9269 Time: 18.62417  | Val Loss: 1.5959 Accuracy: 0.6914\n",
      "Epoch: 98/280 Train Loss: 0.2282 Accuracy: 0.9300 Time: 18.58018  | Val Loss: 1.5777 Accuracy: 0.6965\n",
      "Epoch: 99/280 Train Loss: 0.2312 Accuracy: 0.9306 Time: 18.60877  | Val Loss: 1.5607 Accuracy: 0.6919\n",
      "Epoch: 100/280 Train Loss: 0.2307 Accuracy: 0.9305 Time: 18.63705  | Val Loss: 1.5727 Accuracy: 0.6954\n",
      "Epoch: 101/280 Train Loss: 0.2212 Accuracy: 0.9335 Time: 18.60027  | Val Loss: 1.6297 Accuracy: 0.6910\n",
      "Epoch: 102/280 Train Loss: 0.2119 Accuracy: 0.9350 Time: 18.73270  | Val Loss: 1.6835 Accuracy: 0.6846\n",
      "Epoch: 103/280 Train Loss: 0.2134 Accuracy: 0.9363 Time: 18.72296  | Val Loss: 1.6354 Accuracy: 0.6843\n",
      "Epoch: 104/280 Train Loss: 0.2050 Accuracy: 0.9389 Time: 18.59923  | Val Loss: 1.6614 Accuracy: 0.6861\n",
      "Epoch: 105/280 Train Loss: 0.2090 Accuracy: 0.9370 Time: 18.55169  | Val Loss: 1.7148 Accuracy: 0.6840\n",
      "Epoch: 106/280 Train Loss: 0.2008 Accuracy: 0.9393 Time: 18.63361  | Val Loss: 1.6338 Accuracy: 0.6923\n",
      "Epoch: 107/280 Train Loss: 0.1922 Accuracy: 0.9427 Time: 18.54399  | Val Loss: 1.6822 Accuracy: 0.6905\n",
      "Epoch: 108/280 Train Loss: 0.1927 Accuracy: 0.9412 Time: 18.63267  | Val Loss: 1.7382 Accuracy: 0.6856\n",
      "Epoch: 109/280 Train Loss: 0.1859 Accuracy: 0.9445 Time: 18.53441  | Val Loss: 1.6727 Accuracy: 0.6901\n",
      "Epoch: 110/280 Train Loss: 0.1819 Accuracy: 0.9448 Time: 18.51522  | Val Loss: 1.7962 Accuracy: 0.6784\n",
      "Epoch: 111/280 Train Loss: 0.1850 Accuracy: 0.9446 Time: 18.52429  | Val Loss: 1.7470 Accuracy: 0.6858\n",
      "Epoch: 112/280 Train Loss: 0.1787 Accuracy: 0.9455 Time: 18.55230  | Val Loss: 1.8480 Accuracy: 0.6768\n",
      "Epoch: 113/280 Train Loss: 0.1799 Accuracy: 0.9461 Time: 18.52895  | Val Loss: 1.8753 Accuracy: 0.6752\n",
      "Epoch: 114/280 Train Loss: 0.1724 Accuracy: 0.9471 Time: 18.54324  | Val Loss: 1.7673 Accuracy: 0.6825\n",
      "Epoch: 115/280 Train Loss: 0.1688 Accuracy: 0.9490 Time: 18.52320  | Val Loss: 1.7540 Accuracy: 0.6875\n",
      "Epoch: 116/280 Train Loss: 0.1682 Accuracy: 0.9498 Time: 18.51571  | Val Loss: 1.7578 Accuracy: 0.6813\n",
      "Epoch: 117/280 Train Loss: 0.1693 Accuracy: 0.9493 Time: 18.52986  | Val Loss: 1.6699 Accuracy: 0.6894\n",
      "Epoch: 118/280 Train Loss: 0.1655 Accuracy: 0.9513 Time: 18.51617  | Val Loss: 1.7407 Accuracy: 0.6839\n",
      "Epoch: 119/280 Train Loss: 0.1665 Accuracy: 0.9501 Time: 18.50909  | Val Loss: 1.6908 Accuracy: 0.6941\n",
      "Epoch: 120/280 Train Loss: 0.1609 Accuracy: 0.9515 Time: 18.52238  | Val Loss: 1.7695 Accuracy: 0.6879\n",
      "Epoch: 121/280 Train Loss: 0.1636 Accuracy: 0.9518 Time: 18.55951  | Val Loss: 1.7522 Accuracy: 0.6849\n",
      "Epoch: 122/280 Train Loss: 0.1550 Accuracy: 0.9522 Time: 18.53250  | Val Loss: 1.8788 Accuracy: 0.6766\n",
      "Epoch: 123/280 Train Loss: 0.1584 Accuracy: 0.9525 Time: 18.51587  | Val Loss: 1.7106 Accuracy: 0.6911\n",
      "Epoch: 124/280 Train Loss: 0.1558 Accuracy: 0.9532 Time: 18.52147  | Val Loss: 1.7923 Accuracy: 0.6873\n",
      "Epoch: 125/280 Train Loss: 0.1556 Accuracy: 0.9535 Time: 18.54331  | Val Loss: 1.7623 Accuracy: 0.6902\n",
      "Epoch: 126/280 Train Loss: 0.1507 Accuracy: 0.9551 Time: 18.58245  | Val Loss: 1.7727 Accuracy: 0.6873\n",
      "Epoch: 127/280 Train Loss: 0.1524 Accuracy: 0.9550 Time: 18.57914  | Val Loss: 1.7658 Accuracy: 0.6880\n",
      "Epoch: 128/280 Train Loss: 0.1470 Accuracy: 0.9566 Time: 18.55024  | Val Loss: 1.7564 Accuracy: 0.6854\n",
      "Epoch: 129/280 Train Loss: 0.1478 Accuracy: 0.9553 Time: 18.52657  | Val Loss: 1.7368 Accuracy: 0.6871\n",
      "Epoch: 130/280 Train Loss: 0.1485 Accuracy: 0.9566 Time: 18.60121  | Val Loss: 1.8120 Accuracy: 0.6777\n",
      "Epoch: 131/280 Train Loss: 0.1486 Accuracy: 0.9558 Time: 18.51646  | Val Loss: 1.7302 Accuracy: 0.6931\n",
      "Epoch: 132/280 Train Loss: 0.1459 Accuracy: 0.9568 Time: 18.51578  | Val Loss: 1.7619 Accuracy: 0.6865\n",
      "Epoch: 133/280 Train Loss: 0.1425 Accuracy: 0.9574 Time: 18.53592  | Val Loss: 1.8241 Accuracy: 0.6808\n",
      "Epoch: 134/280 Train Loss: 0.1421 Accuracy: 0.9572 Time: 18.53618  | Val Loss: 1.8312 Accuracy: 0.6822\n",
      "Epoch: 135/280 Train Loss: 0.1390 Accuracy: 0.9587 Time: 18.50965  | Val Loss: 1.7315 Accuracy: 0.6904\n",
      "Epoch: 136/280 Train Loss: 0.1384 Accuracy: 0.9589 Time: 18.51280  | Val Loss: 1.7789 Accuracy: 0.6858\n",
      "Epoch: 137/280 Train Loss: 0.1412 Accuracy: 0.9578 Time: 18.50273  | Val Loss: 1.8688 Accuracy: 0.6780\n",
      "Epoch: 138/280 Train Loss: 0.1388 Accuracy: 0.9584 Time: 18.55098  | Val Loss: 1.8886 Accuracy: 0.6741\n",
      "Epoch: 139/280 Train Loss: 0.1403 Accuracy: 0.9579 Time: 18.51105  | Val Loss: 1.8074 Accuracy: 0.6805\n",
      "Epoch: 140/280 Train Loss: 0.1330 Accuracy: 0.9603 Time: 18.50761  | Val Loss: 1.8290 Accuracy: 0.6818\n",
      "Epoch: 141/280 Train Loss: 0.1068 Accuracy: 0.9691 Time: 18.51172  | Val Loss: 1.7259 Accuracy: 0.6956\n",
      "Epoch: 142/280 Train Loss: 0.0885 Accuracy: 0.9741 Time: 18.55246  | Val Loss: 1.7185 Accuracy: 0.6960\n",
      "Epoch: 143/280 Train Loss: 0.0820 Accuracy: 0.9762 Time: 18.58570  | Val Loss: 1.7907 Accuracy: 0.6920\n",
      "Epoch: 144/280 Train Loss: 0.0787 Accuracy: 0.9766 Time: 18.53028  | Val Loss: 1.7978 Accuracy: 0.6928\n",
      "Epoch: 145/280 Train Loss: 0.0731 Accuracy: 0.9781 Time: 18.57850  | Val Loss: 1.7783 Accuracy: 0.6968\n",
      "Epoch: 146/280 Train Loss: 0.0699 Accuracy: 0.9794 Time: 18.57360  | Val Loss: 1.7599 Accuracy: 0.6967\n",
      "Epoch: 147/280 Train Loss: 0.0711 Accuracy: 0.9789 Time: 18.52575  | Val Loss: 1.8336 Accuracy: 0.6931\n",
      "Epoch: 148/280 Train Loss: 0.0652 Accuracy: 0.9807 Time: 18.51776  | Val Loss: 1.7924 Accuracy: 0.6963\n",
      "Epoch: 149/280 Train Loss: 0.0642 Accuracy: 0.9820 Time: 18.52132  | Val Loss: 1.7936 Accuracy: 0.6976\n",
      "Epoch: 150/280 Train Loss: 0.0606 Accuracy: 0.9823 Time: 18.51728  | Val Loss: 1.7833 Accuracy: 0.6992\n",
      "Epoch: 151/280 Train Loss: 0.0618 Accuracy: 0.9821 Time: 18.50145  | Val Loss: 1.8452 Accuracy: 0.6961\n",
      "Epoch: 152/280 Train Loss: 0.0613 Accuracy: 0.9825 Time: 18.50396  | Val Loss: 1.8127 Accuracy: 0.6970\n",
      "Epoch: 153/280 Train Loss: 0.0628 Accuracy: 0.9820 Time: 18.50857  | Val Loss: 1.8165 Accuracy: 0.6970\n",
      "Epoch: 154/280 Train Loss: 0.0560 Accuracy: 0.9836 Time: 18.50674  | Val Loss: 1.8154 Accuracy: 0.6981\n",
      "Epoch: 155/280 Train Loss: 0.0588 Accuracy: 0.9834 Time: 18.50777  | Val Loss: 1.8506 Accuracy: 0.6947\n",
      "Epoch: 156/280 Train Loss: 0.0560 Accuracy: 0.9834 Time: 18.50009  | Val Loss: 1.8230 Accuracy: 0.6974\n",
      "Early stop at epoch 156!\n",
      "#Parameter: 21328292 Accuracy: 0.6984\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet34_config\n",
    "\n",
    "net, resnet34_stats = train_net(\n",
    "    resnet34_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-2,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "faac6618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/280 Train Loss: 4.2356 Accuracy: 0.0535 Time: 25.78100  | Val Loss: 3.8993 Accuracy: 0.0968\n",
      "Epoch: 2/280 Train Loss: 3.8111 Accuracy: 0.1086 Time: 25.81513  | Val Loss: 3.6599 Accuracy: 0.1308\n",
      "Epoch: 3/280 Train Loss: 3.5581 Accuracy: 0.1477 Time: 25.81952  | Val Loss: 3.2972 Accuracy: 0.1979\n",
      "Epoch: 4/280 Train Loss: 3.3423 Accuracy: 0.1879 Time: 25.80898  | Val Loss: 3.0750 Accuracy: 0.2366\n",
      "Epoch: 5/280 Train Loss: 3.1601 Accuracy: 0.2194 Time: 25.84093  | Val Loss: 2.9515 Accuracy: 0.2616\n",
      "Epoch: 6/280 Train Loss: 3.0066 Accuracy: 0.2509 Time: 25.83315  | Val Loss: 2.9260 Accuracy: 0.2747\n",
      "Epoch: 7/280 Train Loss: 2.9004 Accuracy: 0.2720 Time: 25.83283  | Val Loss: 2.7741 Accuracy: 0.2981\n",
      "Epoch: 8/280 Train Loss: 2.7895 Accuracy: 0.2908 Time: 25.86661  | Val Loss: 2.5776 Accuracy: 0.3361\n",
      "Epoch: 9/280 Train Loss: 2.6843 Accuracy: 0.3133 Time: 25.84748  | Val Loss: 2.5114 Accuracy: 0.3534\n",
      "Epoch: 10/280 Train Loss: 2.5933 Accuracy: 0.3313 Time: 25.84282  | Val Loss: 2.4566 Accuracy: 0.3579\n",
      "Epoch: 11/280 Train Loss: 2.5129 Accuracy: 0.3476 Time: 25.81353  | Val Loss: 2.3982 Accuracy: 0.3763\n",
      "Epoch: 12/280 Train Loss: 2.4304 Accuracy: 0.3660 Time: 25.88560  | Val Loss: 2.3076 Accuracy: 0.3982\n",
      "Epoch: 13/280 Train Loss: 2.3709 Accuracy: 0.3791 Time: 25.83450  | Val Loss: 2.1226 Accuracy: 0.4359\n",
      "Epoch: 14/280 Train Loss: 2.3172 Accuracy: 0.3901 Time: 25.85568  | Val Loss: 2.3029 Accuracy: 0.3976\n",
      "Epoch: 15/280 Train Loss: 2.2570 Accuracy: 0.4036 Time: 25.85086  | Val Loss: 2.3417 Accuracy: 0.3932\n",
      "Epoch: 16/280 Train Loss: 2.2072 Accuracy: 0.4169 Time: 25.85415  | Val Loss: 2.3210 Accuracy: 0.4011\n",
      "Epoch: 17/280 Train Loss: 2.1704 Accuracy: 0.4253 Time: 25.81713  | Val Loss: 2.1505 Accuracy: 0.4264\n",
      "Epoch: 18/280 Train Loss: 2.1365 Accuracy: 0.4311 Time: 25.84714  | Val Loss: 2.2659 Accuracy: 0.4135\n",
      "Epoch: 19/280 Train Loss: 2.0991 Accuracy: 0.4427 Time: 25.83448  | Val Loss: 2.0482 Accuracy: 0.4506\n",
      "Epoch: 20/280 Train Loss: 2.0588 Accuracy: 0.4510 Time: 25.87447  | Val Loss: 2.1033 Accuracy: 0.4478\n",
      "Epoch: 21/280 Train Loss: 2.0331 Accuracy: 0.4545 Time: 25.82888  | Val Loss: 1.9996 Accuracy: 0.4673\n",
      "Epoch: 22/280 Train Loss: 1.9863 Accuracy: 0.4673 Time: 25.85394  | Val Loss: 2.0488 Accuracy: 0.4708\n",
      "Epoch: 23/280 Train Loss: 1.9720 Accuracy: 0.4684 Time: 25.86468  | Val Loss: 2.3668 Accuracy: 0.4096\n",
      "Epoch: 24/280 Train Loss: 1.9346 Accuracy: 0.4804 Time: 25.82048  | Val Loss: 1.9126 Accuracy: 0.4865\n",
      "Epoch: 25/280 Train Loss: 1.9104 Accuracy: 0.4871 Time: 25.80563  | Val Loss: 1.9721 Accuracy: 0.4748\n",
      "Epoch: 26/280 Train Loss: 1.8736 Accuracy: 0.4951 Time: 25.80096  | Val Loss: 2.0364 Accuracy: 0.4743\n",
      "Epoch: 27/280 Train Loss: 1.8550 Accuracy: 0.4999 Time: 25.84970  | Val Loss: 1.9474 Accuracy: 0.4878\n",
      "Epoch: 28/280 Train Loss: 1.8362 Accuracy: 0.5028 Time: 25.82255  | Val Loss: 1.8833 Accuracy: 0.4949\n",
      "Epoch: 29/280 Train Loss: 1.8095 Accuracy: 0.5104 Time: 25.82718  | Val Loss: 1.8798 Accuracy: 0.4977\n",
      "Epoch: 30/280 Train Loss: 1.7876 Accuracy: 0.5175 Time: 25.83516  | Val Loss: 1.8491 Accuracy: 0.5136\n",
      "Epoch: 31/280 Train Loss: 1.7600 Accuracy: 0.5228 Time: 25.84063  | Val Loss: 2.3853 Accuracy: 0.4289\n",
      "Epoch: 32/280 Train Loss: 1.7409 Accuracy: 0.5260 Time: 25.89243  | Val Loss: 1.8268 Accuracy: 0.5125\n",
      "Epoch: 33/280 Train Loss: 1.7215 Accuracy: 0.5303 Time: 25.85089  | Val Loss: 1.8280 Accuracy: 0.5137\n",
      "Epoch: 34/280 Train Loss: 1.6985 Accuracy: 0.5370 Time: 25.81841  | Val Loss: 2.0577 Accuracy: 0.4877\n",
      "Epoch: 35/280 Train Loss: 1.6811 Accuracy: 0.5406 Time: 25.83162  | Val Loss: 1.8828 Accuracy: 0.5107\n",
      "Epoch: 36/280 Train Loss: 1.6663 Accuracy: 0.5469 Time: 25.79373  | Val Loss: 1.9053 Accuracy: 0.5070\n",
      "Epoch: 37/280 Train Loss: 1.6500 Accuracy: 0.5508 Time: 25.83301  | Val Loss: 1.8525 Accuracy: 0.5184\n",
      "Epoch: 38/280 Train Loss: 1.6420 Accuracy: 0.5502 Time: 25.89354  | Val Loss: 1.9801 Accuracy: 0.4994\n",
      "Epoch: 39/280 Train Loss: 1.6269 Accuracy: 0.5555 Time: 25.81321  | Val Loss: 1.8503 Accuracy: 0.5113\n",
      "Epoch: 40/280 Train Loss: 1.6193 Accuracy: 0.5584 Time: 25.78995  | Val Loss: 2.2821 Accuracy: 0.4640\n",
      "Epoch: 41/280 Train Loss: 1.6047 Accuracy: 0.5611 Time: 25.81489  | Val Loss: 1.8738 Accuracy: 0.5195\n",
      "Epoch: 42/280 Train Loss: 1.5942 Accuracy: 0.5647 Time: 25.83488  | Val Loss: 2.0496 Accuracy: 0.4859\n",
      "Epoch: 43/280 Train Loss: 1.5802 Accuracy: 0.5665 Time: 25.83844  | Val Loss: 1.9252 Accuracy: 0.5100\n",
      "Epoch: 44/280 Train Loss: 1.5616 Accuracy: 0.5711 Time: 25.81417  | Val Loss: 1.7704 Accuracy: 0.5331\n",
      "Epoch: 45/280 Train Loss: 1.5622 Accuracy: 0.5726 Time: 25.84667  | Val Loss: 2.1097 Accuracy: 0.4848\n",
      "Epoch: 46/280 Train Loss: 1.5560 Accuracy: 0.5783 Time: 25.87113  | Val Loss: 1.8077 Accuracy: 0.5306\n",
      "Epoch: 47/280 Train Loss: 1.5310 Accuracy: 0.5804 Time: 25.84727  | Val Loss: 1.6718 Accuracy: 0.5539\n",
      "Epoch: 48/280 Train Loss: 1.5317 Accuracy: 0.5803 Time: 25.85324  | Val Loss: 1.6850 Accuracy: 0.5524\n",
      "Epoch: 49/280 Train Loss: 1.5247 Accuracy: 0.5830 Time: 25.79556  | Val Loss: 1.7302 Accuracy: 0.5428\n",
      "Epoch: 50/280 Train Loss: 1.5189 Accuracy: 0.5822 Time: 25.82465  | Val Loss: 2.6209 Accuracy: 0.4618\n",
      "Epoch: 51/280 Train Loss: 1.5107 Accuracy: 0.5864 Time: 25.88117  | Val Loss: 1.7614 Accuracy: 0.5354\n",
      "Epoch: 52/280 Train Loss: 1.4927 Accuracy: 0.5912 Time: 25.87205  | Val Loss: 1.7521 Accuracy: 0.5529\n",
      "Epoch: 53/280 Train Loss: 1.4897 Accuracy: 0.5937 Time: 25.87106  | Val Loss: 1.8560 Accuracy: 0.5261\n",
      "Epoch: 54/280 Train Loss: 1.4933 Accuracy: 0.5922 Time: 25.82046  | Val Loss: 1.7257 Accuracy: 0.5516\n",
      "Epoch: 55/280 Train Loss: 1.4846 Accuracy: 0.5925 Time: 25.82625  | Val Loss: 1.7003 Accuracy: 0.5475\n",
      "Epoch: 56/280 Train Loss: 1.4761 Accuracy: 0.5968 Time: 25.84299  | Val Loss: 1.7171 Accuracy: 0.5535\n",
      "Epoch: 57/280 Train Loss: 1.4627 Accuracy: 0.5984 Time: 25.84807  | Val Loss: 1.8180 Accuracy: 0.5308\n",
      "Epoch: 58/280 Train Loss: 1.4572 Accuracy: 0.6005 Time: 25.81197  | Val Loss: 1.9246 Accuracy: 0.5205\n",
      "Epoch: 59/280 Train Loss: 1.4542 Accuracy: 0.5993 Time: 25.88496  | Val Loss: 1.8192 Accuracy: 0.5393\n",
      "Epoch: 60/280 Train Loss: 1.4466 Accuracy: 0.6066 Time: 25.78152  | Val Loss: 1.7699 Accuracy: 0.5377\n",
      "Epoch: 61/280 Train Loss: 1.4513 Accuracy: 0.6024 Time: 25.79794  | Val Loss: 1.9165 Accuracy: 0.5326\n",
      "Epoch: 62/280 Train Loss: 1.4543 Accuracy: 0.6029 Time: 25.79786  | Val Loss: 1.8184 Accuracy: 0.5520\n",
      "Epoch: 63/280 Train Loss: 1.4352 Accuracy: 0.6085 Time: 25.83039  | Val Loss: 1.6803 Accuracy: 0.5557\n",
      "Epoch: 64/280 Train Loss: 1.4366 Accuracy: 0.6075 Time: 25.81798  | Val Loss: 1.5645 Accuracy: 0.5831\n",
      "Epoch: 65/280 Train Loss: 1.4271 Accuracy: 0.6110 Time: 25.82536  | Val Loss: 1.6856 Accuracy: 0.5625\n",
      "Epoch: 66/280 Train Loss: 1.4345 Accuracy: 0.6084 Time: 25.80847  | Val Loss: 1.7154 Accuracy: 0.5706\n",
      "Epoch: 67/280 Train Loss: 1.4218 Accuracy: 0.6109 Time: 25.81093  | Val Loss: 1.6798 Accuracy: 0.5524\n",
      "Epoch: 68/280 Train Loss: 1.4163 Accuracy: 0.6096 Time: 25.82460  | Val Loss: 1.7560 Accuracy: 0.5505\n",
      "Epoch: 69/280 Train Loss: 1.4076 Accuracy: 0.6138 Time: 25.81186  | Val Loss: 1.6913 Accuracy: 0.5642\n",
      "Epoch: 70/280 Train Loss: 1.4091 Accuracy: 0.6133 Time: 25.83620  | Val Loss: 1.5537 Accuracy: 0.5827\n",
      "Epoch: 71/280 Train Loss: 1.0380 Accuracy: 0.7047 Time: 25.81789  | Val Loss: 1.3573 Accuracy: 0.6425\n",
      "Epoch: 72/280 Train Loss: 0.9091 Accuracy: 0.7367 Time: 25.83746  | Val Loss: 1.3096 Accuracy: 0.6522\n",
      "Epoch: 73/280 Train Loss: 0.8439 Accuracy: 0.7529 Time: 25.80926  | Val Loss: 1.3522 Accuracy: 0.6496\n",
      "Epoch: 74/280 Train Loss: 0.8044 Accuracy: 0.7659 Time: 25.88177  | Val Loss: 1.3195 Accuracy: 0.6561\n",
      "Epoch: 75/280 Train Loss: 0.7736 Accuracy: 0.7735 Time: 25.85313  | Val Loss: 1.3218 Accuracy: 0.6556\n",
      "Epoch: 76/280 Train Loss: 0.7434 Accuracy: 0.7824 Time: 25.86760  | Val Loss: 1.3143 Accuracy: 0.6639\n",
      "Epoch: 77/280 Train Loss: 0.7166 Accuracy: 0.7908 Time: 25.83605  | Val Loss: 1.3333 Accuracy: 0.6615\n",
      "Epoch: 78/280 Train Loss: 0.6971 Accuracy: 0.7946 Time: 25.83058  | Val Loss: 1.3235 Accuracy: 0.6639\n",
      "Epoch: 79/280 Train Loss: 0.6790 Accuracy: 0.7994 Time: 25.79831  | Val Loss: 1.2793 Accuracy: 0.6751\n",
      "Epoch: 80/280 Train Loss: 0.6642 Accuracy: 0.8019 Time: 25.80686  | Val Loss: 1.3602 Accuracy: 0.6631\n",
      "Epoch: 81/280 Train Loss: 0.6457 Accuracy: 0.8087 Time: 25.82734  | Val Loss: 1.3416 Accuracy: 0.6652\n",
      "Epoch: 82/280 Train Loss: 0.6303 Accuracy: 0.8117 Time: 25.83042  | Val Loss: 1.3387 Accuracy: 0.6716\n",
      "Epoch: 83/280 Train Loss: 0.6121 Accuracy: 0.8171 Time: 25.88489  | Val Loss: 1.3077 Accuracy: 0.6710\n",
      "Epoch: 84/280 Train Loss: 0.5951 Accuracy: 0.8205 Time: 25.82818  | Val Loss: 1.3582 Accuracy: 0.6685\n",
      "Epoch: 85/280 Train Loss: 0.5813 Accuracy: 0.8258 Time: 25.83445  | Val Loss: 1.2978 Accuracy: 0.6817\n",
      "Epoch: 86/280 Train Loss: 0.5662 Accuracy: 0.8289 Time: 25.83520  | Val Loss: 1.3252 Accuracy: 0.6768\n",
      "Epoch: 87/280 Train Loss: 0.5517 Accuracy: 0.8332 Time: 25.79587  | Val Loss: 1.3769 Accuracy: 0.6741\n",
      "Epoch: 88/280 Train Loss: 0.5510 Accuracy: 0.8355 Time: 25.83720  | Val Loss: 1.3436 Accuracy: 0.6740\n",
      "Epoch: 89/280 Train Loss: 0.5378 Accuracy: 0.8382 Time: 25.84190  | Val Loss: 1.3451 Accuracy: 0.6762\n",
      "Epoch: 90/280 Train Loss: 0.5220 Accuracy: 0.8421 Time: 25.85907  | Val Loss: 1.3643 Accuracy: 0.6772\n",
      "Epoch: 91/280 Train Loss: 0.5092 Accuracy: 0.8477 Time: 25.87612  | Val Loss: 1.3519 Accuracy: 0.6759\n",
      "Epoch: 92/280 Train Loss: 0.5029 Accuracy: 0.8475 Time: 25.90953  | Val Loss: 1.4244 Accuracy: 0.6716\n",
      "Epoch: 93/280 Train Loss: 0.4822 Accuracy: 0.8539 Time: 25.86668  | Val Loss: 1.4440 Accuracy: 0.6721\n",
      "Epoch: 94/280 Train Loss: 0.4819 Accuracy: 0.8548 Time: 25.82532  | Val Loss: 1.3728 Accuracy: 0.6788\n",
      "Epoch: 95/280 Train Loss: 0.4691 Accuracy: 0.8589 Time: 25.79415  | Val Loss: 1.4143 Accuracy: 0.6762\n",
      "Epoch: 96/280 Train Loss: 0.4656 Accuracy: 0.8593 Time: 25.81711  | Val Loss: 1.4057 Accuracy: 0.6768\n",
      "Epoch: 97/280 Train Loss: 0.4542 Accuracy: 0.8611 Time: 25.81645  | Val Loss: 1.3816 Accuracy: 0.6833\n",
      "Epoch: 98/280 Train Loss: 0.4397 Accuracy: 0.8658 Time: 25.82114  | Val Loss: 1.4504 Accuracy: 0.6738\n",
      "Epoch: 99/280 Train Loss: 0.4419 Accuracy: 0.8662 Time: 25.84550  | Val Loss: 1.4479 Accuracy: 0.6724\n",
      "Epoch: 100/280 Train Loss: 0.4253 Accuracy: 0.8705 Time: 25.81682  | Val Loss: 1.4506 Accuracy: 0.6770\n",
      "Epoch: 101/280 Train Loss: 0.4194 Accuracy: 0.8735 Time: 25.79290  | Val Loss: 1.4916 Accuracy: 0.6713\n",
      "Epoch: 102/280 Train Loss: 0.4159 Accuracy: 0.8739 Time: 25.83681  | Val Loss: 1.4242 Accuracy: 0.6843\n",
      "Epoch: 103/280 Train Loss: 0.4049 Accuracy: 0.8772 Time: 25.80618  | Val Loss: 1.4789 Accuracy: 0.6784\n",
      "Epoch: 104/280 Train Loss: 0.3972 Accuracy: 0.8786 Time: 25.80833  | Val Loss: 1.4696 Accuracy: 0.6795\n",
      "Epoch: 105/280 Train Loss: 0.4021 Accuracy: 0.8793 Time: 25.87966  | Val Loss: 1.4732 Accuracy: 0.6728\n",
      "Epoch: 106/280 Train Loss: 0.3828 Accuracy: 0.8823 Time: 25.82112  | Val Loss: 1.4459 Accuracy: 0.6793\n",
      "Epoch: 107/280 Train Loss: 0.3705 Accuracy: 0.8875 Time: 25.81404  | Val Loss: 1.4901 Accuracy: 0.6786\n",
      "Epoch: 108/280 Train Loss: 0.3697 Accuracy: 0.8854 Time: 25.84809  | Val Loss: 1.5068 Accuracy: 0.6774\n",
      "Epoch: 109/280 Train Loss: 0.3745 Accuracy: 0.8873 Time: 25.79992  | Val Loss: 1.4474 Accuracy: 0.6847\n",
      "Epoch: 110/280 Train Loss: 0.3586 Accuracy: 0.8904 Time: 25.86303  | Val Loss: 1.5184 Accuracy: 0.6739\n",
      "Epoch: 111/280 Train Loss: 0.3564 Accuracy: 0.8914 Time: 25.84590  | Val Loss: 1.5447 Accuracy: 0.6743\n",
      "Epoch: 112/280 Train Loss: 0.3513 Accuracy: 0.8937 Time: 25.85937  | Val Loss: 1.5498 Accuracy: 0.6767\n",
      "Epoch: 113/280 Train Loss: 0.3494 Accuracy: 0.8935 Time: 25.86626  | Val Loss: 1.5544 Accuracy: 0.6732\n",
      "Epoch: 114/280 Train Loss: 0.3456 Accuracy: 0.8939 Time: 25.83160  | Val Loss: 1.5464 Accuracy: 0.6764\n",
      "Epoch: 115/280 Train Loss: 0.3346 Accuracy: 0.8965 Time: 25.84765  | Val Loss: 1.5720 Accuracy: 0.6774\n",
      "Epoch: 116/280 Train Loss: 0.3366 Accuracy: 0.8959 Time: 25.84224  | Val Loss: 1.5507 Accuracy: 0.6756\n",
      "Epoch: 117/280 Train Loss: 0.3302 Accuracy: 0.8991 Time: 25.82224  | Val Loss: 1.6046 Accuracy: 0.6739\n",
      "Epoch: 118/280 Train Loss: 0.3250 Accuracy: 0.9007 Time: 25.85299  | Val Loss: 1.5817 Accuracy: 0.6769\n",
      "Epoch: 119/280 Train Loss: 0.3185 Accuracy: 0.9043 Time: 25.80789  | Val Loss: 1.6444 Accuracy: 0.6638\n",
      "Epoch: 120/280 Train Loss: 0.3174 Accuracy: 0.9038 Time: 25.79394  | Val Loss: 1.5426 Accuracy: 0.6790\n",
      "Epoch: 121/280 Train Loss: 0.3139 Accuracy: 0.9046 Time: 25.82179  | Val Loss: 1.5840 Accuracy: 0.6725\n",
      "Epoch: 122/280 Train Loss: 0.3067 Accuracy: 0.9061 Time: 25.84673  | Val Loss: 1.5275 Accuracy: 0.6783\n",
      "Epoch: 123/280 Train Loss: 0.2992 Accuracy: 0.9079 Time: 25.80914  | Val Loss: 1.5832 Accuracy: 0.6739\n",
      "Epoch: 124/280 Train Loss: 0.2988 Accuracy: 0.9100 Time: 25.90509  | Val Loss: 1.4960 Accuracy: 0.6802\n",
      "Epoch: 125/280 Train Loss: 0.3072 Accuracy: 0.9051 Time: 25.84403  | Val Loss: 1.5571 Accuracy: 0.6800\n",
      "Epoch: 126/280 Train Loss: 0.2950 Accuracy: 0.9073 Time: 25.81567  | Val Loss: 1.6164 Accuracy: 0.6733\n",
      "Epoch: 127/280 Train Loss: 0.2903 Accuracy: 0.9116 Time: 25.80960  | Val Loss: 1.5761 Accuracy: 0.6736\n",
      "Epoch: 128/280 Train Loss: 0.2877 Accuracy: 0.9119 Time: 25.79236  | Val Loss: 1.6725 Accuracy: 0.6741\n",
      "Epoch: 129/280 Train Loss: 0.2864 Accuracy: 0.9137 Time: 25.80167  | Val Loss: 1.5572 Accuracy: 0.6839\n",
      "Epoch: 130/280 Train Loss: 0.2765 Accuracy: 0.9163 Time: 25.80847  | Val Loss: 1.6242 Accuracy: 0.6771\n",
      "Epoch: 131/280 Train Loss: 0.2759 Accuracy: 0.9159 Time: 25.83000  | Val Loss: 1.7380 Accuracy: 0.6623\n",
      "Epoch: 132/280 Train Loss: 0.2765 Accuracy: 0.9146 Time: 25.82791  | Val Loss: 1.6855 Accuracy: 0.6703\n",
      "Epoch: 133/280 Train Loss: 0.2769 Accuracy: 0.9157 Time: 25.84014  | Val Loss: 1.6317 Accuracy: 0.6770\n",
      "Epoch: 134/280 Train Loss: 0.2716 Accuracy: 0.9167 Time: 25.82553  | Val Loss: 1.5725 Accuracy: 0.6841\n",
      "Epoch: 135/280 Train Loss: 0.2640 Accuracy: 0.9194 Time: 25.81010  | Val Loss: 1.7423 Accuracy: 0.6704\n",
      "Epoch: 136/280 Train Loss: 0.2691 Accuracy: 0.9192 Time: 25.82252  | Val Loss: 1.7122 Accuracy: 0.6658\n",
      "Epoch: 137/280 Train Loss: 0.2606 Accuracy: 0.9202 Time: 25.82315  | Val Loss: 1.6448 Accuracy: 0.6745\n",
      "Epoch: 138/280 Train Loss: 0.2593 Accuracy: 0.9204 Time: 25.83878  | Val Loss: 1.7323 Accuracy: 0.6668\n",
      "Epoch: 139/280 Train Loss: 0.2528 Accuracy: 0.9234 Time: 25.79951  | Val Loss: 1.6365 Accuracy: 0.6773\n",
      "Epoch: 140/280 Train Loss: 0.2540 Accuracy: 0.9226 Time: 25.83496  | Val Loss: 1.6528 Accuracy: 0.6769\n",
      "Epoch: 141/280 Train Loss: 0.2102 Accuracy: 0.9360 Time: 25.83195  | Val Loss: 1.6660 Accuracy: 0.6785\n",
      "Epoch: 142/280 Train Loss: 0.1833 Accuracy: 0.9447 Time: 25.82707  | Val Loss: 1.6290 Accuracy: 0.6845\n",
      "Epoch: 143/280 Train Loss: 0.1739 Accuracy: 0.9472 Time: 25.83066  | Val Loss: 1.6875 Accuracy: 0.6775\n",
      "Epoch: 144/280 Train Loss: 0.1710 Accuracy: 0.9478 Time: 25.82990  | Val Loss: 1.6599 Accuracy: 0.6850\n",
      "Epoch: 145/280 Train Loss: 0.1643 Accuracy: 0.9508 Time: 25.80279  | Val Loss: 1.6580 Accuracy: 0.6865\n",
      "Epoch: 146/280 Train Loss: 0.1603 Accuracy: 0.9521 Time: 25.82473  | Val Loss: 1.6980 Accuracy: 0.6831\n",
      "Epoch: 147/280 Train Loss: 0.1587 Accuracy: 0.9525 Time: 25.86038  | Val Loss: 1.6489 Accuracy: 0.6861\n",
      "Epoch: 148/280 Train Loss: 0.1557 Accuracy: 0.9530 Time: 25.80238  | Val Loss: 1.6250 Accuracy: 0.6874\n",
      "Epoch: 149/280 Train Loss: 0.1498 Accuracy: 0.9548 Time: 25.82781  | Val Loss: 1.6496 Accuracy: 0.6878\n",
      "Epoch: 150/280 Train Loss: 0.1488 Accuracy: 0.9558 Time: 25.82187  | Val Loss: 1.6842 Accuracy: 0.6841\n",
      "Epoch: 151/280 Train Loss: 0.1441 Accuracy: 0.9578 Time: 25.84628  | Val Loss: 1.6637 Accuracy: 0.6887\n",
      "Epoch: 152/280 Train Loss: 0.1502 Accuracy: 0.9540 Time: 25.84014  | Val Loss: 1.6911 Accuracy: 0.6850\n",
      "Epoch: 153/280 Train Loss: 0.1480 Accuracy: 0.9556 Time: 25.82784  | Val Loss: 1.7160 Accuracy: 0.6831\n",
      "Epoch: 154/280 Train Loss: 0.1400 Accuracy: 0.9564 Time: 25.81849  | Val Loss: 1.6998 Accuracy: 0.6851\n",
      "Epoch: 155/280 Train Loss: 0.1412 Accuracy: 0.9575 Time: 25.86069  | Val Loss: 1.6797 Accuracy: 0.6893\n",
      "Epoch: 156/280 Train Loss: 0.1387 Accuracy: 0.9593 Time: 25.80304  | Val Loss: 1.6642 Accuracy: 0.6918\n",
      "Epoch: 157/280 Train Loss: 0.1375 Accuracy: 0.9585 Time: 25.83860  | Val Loss: 1.7088 Accuracy: 0.6840\n",
      "Epoch: 158/280 Train Loss: 0.1329 Accuracy: 0.9606 Time: 25.82509  | Val Loss: 1.7358 Accuracy: 0.6868\n",
      "Epoch: 159/280 Train Loss: 0.1317 Accuracy: 0.9607 Time: 25.82640  | Val Loss: 1.7204 Accuracy: 0.6863\n",
      "Early stop at epoch 159!\n",
      "#Parameter: 23705252 Accuracy: 0.6751\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet50_config\n",
    "\n",
    "net, resnet50_stats = train_net(\n",
    "    resnet50_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-2,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "432e2801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = resnet18_stats.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(resnet18_stats[field], label=\"Resnet18\", linestyle=\"--\")\n",
    "    plt.plot(resnet34_stats[field], label=\"Resnet34\")\n",
    "    plt.plot(resnet50_stats[field], label=\"Resnet50\", linestyle=\"--\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e567083",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
